Datathon NSI Mentors’ Guidelines – Economic Time Series Prediction

Posted 1 CommentPosted in GD2018 Mentors, Mentors

In this article the mentors give some preliminary guidelines, advice and suggestions to the participants for the case. Every mentor should write their name and chat name in the beginning of their texts, so that there are no mix-ups with the other menthors. By rules it is essential to follow CRISP-DM methodology (http://www.sv-europe.com/crisp-dm-methodology/). The DSS […]

Datathon Telenor Mentors’ Guidelines – On TelCo predictions

Posted Leave a commentPosted in GD2018 Mentors, Mentors

In this article the mentors give some preliminary guidelines, advice and suggestions to the participants for the case. Every mentor should write their name and chat name in the beginning of their texts, so that there are no mix-ups with the other menthors. By rules it is essential to follow CRISP-DM methodology (http://www.sv-europe.com/crisp-dm-methodology/). The DSS […]

Datathon Sofia Air Mentors’ Guidelines – On IOT Prediction

Posted Leave a commentPosted in GD2018 Mentors, Mentors

In this article the mentors give some preliminary guidelines, advice and suggestions to the participants for the case. Every mentor should write their name and chat name in the beginning of their texts, so that there are no mix-ups with the other menthors. By rules it is essential to follow CRISP-DM methodology (http://www.sv-europe.com/crisp-dm-methodology/). The DSS […]

Datathon Kaufland Mentors’ Guidelines – On Predictive Maintenance

Posted Leave a commentPosted in GD2018 Mentors, Mentors

In this article, the mentors give some preliminary guidelines, advice, and suggestions to the participants for the case. Every mentor should write their name and chat name at the beginning of their texts so that there are no mix-ups with the other mentors. By rules, it is essential to follow CRISP-DM methodology (http://www.sv-europe.com/crisp-dm-methodology/). The DSS […]

Datathon Kaufland Solution – Kaufland case – Team3

Posted 1 CommentPosted in Datathons Solutions

In [1]: import s3fs import pandas as pd import matplotlib.pyplot as plt import matplotlib.dates as mdates import seaborn as sns import numpy as np import pywt In [2]: fs = s3fs.S3FileSystem(anon=True) fs.ls(‘datacases/datathon-2018-2/’) Out[2]: [‘datacases/datathon-2018-2/kaufland’, ‘datacases/datathon-2018-2/nsi’, ‘datacases/datathon-2018-2/ontotext’, ‘datacases/datathon-2018-2/telelink’, ‘datacases/datathon-2018-2/telenor’] In [3]: fs.ls(‘datacases/datathon-2018-2/kaufland’) Out[3]: [‘datacases/datathon-2018-2/kaufland/20180820_Kaufland_case_IoT_and_predictive_maintenance_events.xlsx’, ‘datacases/datathon-2018-2/kaufland/20180920_Kaufland_case_IoT_and_predictive_maintenance.csv’, ‘datacases/datathon-2018-2/kaufland/sample_Kaufland_case_IoT_and_predictive_maintenance.csv’] Events¶ In [4]: with fs.open(‘datacases/datathon-2018-2/kaufland/20180820_Kaufland_case_IoT_and_predictive_maintenance_events.xlsx’, ‘rb’) as f: df_events = pd.read_excel(f) In [5]: df_events Out[5]: […]

Datathon Kaufland Solution – Team Total Kaputt! – Why da faQ the machine broke down?

Posted 1 CommentPosted in Prediction systems

What we tried to do to solve the Kaufland case for the Global Datathon 2018. This article just contains our exploratory data analysis in the form of many plots and some explanations. There isn’t any modeling stage described here.

Datathon Ontotext Mentors’ Guidelines – Text Mining Classification

Posted Leave a commentPosted in GD2018 Mentors, Mentors

In this article the mentors give some preliminary guidelines, advice and suggestions to the participants for the case. Every mentor should write their name and chat name in the beginning of their texts, so that there are no mix-ups with the other menthors. By rules it is essential to follow CRISP-DM methodology (http://www.sv-europe.com/crisp-dm-methodology/). The DSS […]

Datathon Kaufland Solution – Predictive Maintenance Based on Sensor Data for Forklifts

Posted 2 CommentsPosted in Prediction systems

Kaufland-Case 1. Business Understanding Industrial vibration analysis is a measurement tool used to identify, predict, and prevent failures. Implementing vibration analysis on the machines will improve the reliability of the machines and lead to better machine efficiency and reduced down time eliminating mechanical or electrical failures.┬áVibration analysis are used┬áto identify faults in machinery, plan machinery […]

Datathon Kaufland Solution – LSTM and EDM Models for Predictive Maintenance

Posted 2 CommentsPosted in Datathons Solutions

In this paper we propose the use of a combination of LSTM and EDM models to address the issue of anomaly classification and prediction in time series data. Working with sensor data for automated storage and retrieval systems for a German hypermarket chain, we show that predictors based on variance and median methods show sufficient promise in the handling of anomalies.